What is Azure Machine Learning Studio?

What is Azure Machine Learning Studio?

Microsoft Azure Machine Learning Studio is a collaborative, drag-and-drop tool you can use to build, test, and deploy predictive analytics solutions on your data. Machine Learning Studio publishes models as web services that can easily be consumed by custom apps or BI tools such as Excel.

The Machine Learning Studio interactive workspace

To develop a predictive analysis model, you typically use data from one or more sources, transform and analyze that data through various data manipulation and statistical functions, and generate a set of results. Developing a model like this is an iterative process. As you modify the various functions and their parameters, your results converge until you are satisfied that you have a trained, effective model.

Azure Machine Learning Studio gives you an interactive, visual workspace to easily build, test, and iterate on a predictive analysis model. You drag-and-drop datasets and analysis modules onto an interactive canvas, connecting them together to form an experiment, which you run in Machine Learning Studio. To iterate on your model design, you edit the experiment, save a copy if desired, and run it again. When you’re ready, you can convert your training experiment to a predictive experiment, and then publish it as a web service so that your model can be accessed by others.

There is no programming required, just visually connecting datasets and modules to construct your predictive analysis model.

There is no programming required, just visually connecting datasets and modules to construct your predictive analysis model.+

Get started with Machine Learning Studio

When you first enter Machine Learning Studio you see the Home page. From here you can view documentation, videos, webinars, and find other valuable resources.

Click the upper-left menu and you’ll see several options.

Cortana Intelligence

Click Cortana Intelligence and you’ll be taken to the home page of the Cortana Intelligence Suite. The Cortana Intelligence Suite is a fully managed big data and advanced analytics suite to transform your data into intelligent action. See the Suite home page for full documentation, including customer stories.

Azure Machine Learning

There are two options here, Home, the page where you started, and Studio.

Click Studio and you’ll be taken to the Azure Machine Learning Studio. First you’ll be asked to sign in using your Microsoft account, or your work or school account. Once signed in, you’ll see the following tabs on the left:

PROJECTS – Collections of experiments, datasets, notebooks, and other resources representing a single project

EXPERIMENTS – Experiments that you have created and run or saved as drafts

WEB SERVICES – Web services that you have deployed from your experiments

NOTEBOOKS – Jupyter notebooks that you have created

DATASETS – Datasets that you have uploaded into Studio

TRAINED MODELS – Models that you have trained in experiments and saved in Studio

SETTINGS – A collection of settings that you can use to configure your account and resources.

Gallery

Click Gallery and you’ll be taken to the Cortana Intelligence Gallery. The Gallery is a place where a community of data scientists and developers share solutions created using components of the Cortana Intelligence Suite.

Components of an experiment

An experiment consists of datasets that provide data to analytical modules, which you connect together to construct a predictive analysis model. Specifically, a valid experiment has these characteristics:

The experiment has at least one dataset and one module

Datasets may be connected only to modules

Modules may be connected to either datasets or other modules

All input ports for modules must have some connection to the data flow

Datasets

A dataset is data that has been uploaded to Machine Learning Studio so that it can be used in the modeling process. A number of sample datasets are included with Machine Learning Studio for you to experiment with, and you can upload more datasets as you need them. Here are some examples of included datasets:

MPG data for various automobiles – Miles per gallon (MPG) values for automobiles identified by number of cylinders, horsepower, etc.

Breast cancer data – Breast cancer diagnosis data.

Forest fires data – Forest fire sizes in northeast Portugal.

As you build an experiment you can choose from the list of datasets available to the left of the canvas.

Modules

A module is an algorithm that you can perform on your data. Machine Learning Studio has a number of modules ranging from data ingress functions to training, scoring, and validation processes. Here are some examples of included modules:

As you build an experiment you can choose from the list of modules available to the left of the canvas.

A module may have a set of parameters that you can use to configure the module’s internal algorithms. When you select a module on the canvas, the module’s parameters are displayed in the Properties pane to the right of the canvas. You can modify the parameters in that pane to tune your model.